CN113438271B - Memory, and method, device and equipment for data transmission management of Internet of things - Google Patents
Memory, and method, device and equipment for data transmission management of Internet of things Download PDFInfo
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Abstract
The invention discloses a memory, a method, a device and equipment for data transmission management of the Internet of things, wherein the method comprises the following steps: further dividing an edge layer in a cloud edge network architecture in a framework model of the Internet of things into an edge infrastructure layer and an edge intelligent device layer; according to different requirements of response speed and processing capacity, dividing the types of data transmitted by the Internet of things into real-time data, near real-time data and non-real-time data; and determining a transmission path of the data to be transmitted in the Internet of things according to a preset rule. The invention can effectively reduce unnecessary transmission of transmission data in the Internet of things, thereby effectively reducing the overall throughput of the Internet of things, improving the overall operation efficiency and finally achieving the purpose of improving the response speed of the Internet of things.
Description
Technical Field
The invention relates to the field of application of the Internet of things, in particular to a memory, and a method, a device and equipment for data transmission management of the Internet of things.
Background
The internet of things mainly depends on sensing layer technology, network layer technology, business and application layer technology (information discovery, intelligent processing, middleware, distributed computation and the like) and the like to connect all things in the world into an information system according to protocol convention, so that the distance between the information system and the physical world is shortened.
The reliability of data transmission is seriously influenced by complex network characteristics such as dynamics, cooperativity, diversity, mobility, openness and the like faced by the environment of the internet of things. The primary factor affecting reliable data transmission is the selection and construction of the architecture of the internet of things system. The correct and effective architecture of the internet of things system can ensure that data is transmitted to the correct position at the correct time.
The inventor finds that the internet of things in the prior art has at least the following problems:
at present, because a cloud server in a cloud center is far away from a terminal, the data transmission rate of an ultra-long distance is low, a large amount of network bandwidth can be occupied, the transmission delay is increased, and the system cannot respond to emergency situations in real time, so that the service decision efficiency is reduced, the waiting time of a user is too long, and the user experience quality rapidly slides down.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention aims to solve the problem that the response speed of the Internet of things is not ideal enough.
The invention provides a data transmission management method of an Internet of things, which comprises the following steps:
s01, further dividing an edge layer in a cloud edge network architecture in a framework model of the Internet of things into an edge infrastructure layer and an edge intelligent device layer; the edge intelligent devices of the edge intelligent device layer comprise various mobile intelligent devices in the edge layer, and the rest edge devices in the edge layer are edge infrastructure network facilities in the edge infrastructure layer;
s02, dividing the types of the data transmitted by the Internet of things into real-time data, near real-time data and non-real-time data according to different requirements of response speed and processing capacity; wherein the real-time data comprises data which needs to be processed by the edge intelligent device; the near real-time data comprises data requiring processing by an edge infrastructure in the edge infrastructure layer; the non-real-time data includes data that does not belong to either the real-time data or the near real-time data;
s03, determining a transmission path of data to be transmitted in the Internet of things according to a preset rule; the preset rules include:
when the data to be transmitted is real-time data, forwarding the data to be transmitted to the nearest edge intelligent equipment in the edge intelligent equipment layer for processing; the edge intelligent equipment is used for generating result data and data to be transmitted of near real-time data or data to be transmitted of non-real-time data according to the data to be transmitted;
when the data to be transmitted is not real-time data, judging whether the data to be transmitted is near real-time data or not;
when the data to be transmitted is near real-time data, the near real-time data is transmitted to an edge network infrastructure in the edge network infrastructure layer for processing, and the edge network infrastructure is used for generating result data or the data to be transmitted of non-real-time data;
and when the data to be transmitted is not near real-time data, deleting the data to be transmitted as non-real-time data or transmitting the data to be transmitted to a cloud end layer for calculation processing.
In the present invention, the method further comprises:
respectively designing a maximum resource allocation strategy of each edge intelligent device governed by an edge intelligent gateway according to the total buffer area length and the output link capacity of the edge intelligent gateway;
respectively constructing an end-to-end total delay function for all data packets on each edge intelligent device according to data transmission delay, processing delay and queuing delay;
respectively constructing a data accumulated delivery rate function corresponding to each edge intelligent device according to the average queue length of the data packets on the edge intelligent device and the required buffer pool length;
and aiming at all edge intelligent devices in the scope governed by the edge intelligent gateway, constructing a resource allocation optimal scheme which simultaneously meets the conditions of maximizing the data accumulated delivery rate, minimizing the total time delay and maximizing the utilization rate of the total buffer area length and the output link capacity of the edge intelligent gateway.
In the present invention, the method further comprises:
the link scheduling program determines a preset number of links according to user requirements and constructs a self-adaptive optimization function of each link;
determining the priority corresponding to the number to be transmitted according to the transmission rate requirement and the queuing time delay requirement of the number to be transmitted; the priorities are divided according to the data transmission rate requirement and the queuing delay requirement;
and respectively calculating the bandwidth or the buffer length corresponding to each link and allocating the bandwidth or the buffer length to the link corresponding to the data to be transmitted according to the priority of the data to be transmitted.
In the present invention, the designing the maximum resource allocation policy of each edge intelligent device governed by the edge intelligent gateway according to the total buffer length and the output link capacity of the edge intelligent gateway includes:
setting the total buffer area length and the output link capacity of the edge intelligent gateway node to be L and C respectively, wherein the edge intelligent device layer is connected with M edge intelligent devices; p size Is the size of one packet on each edge smart device; bd i 、Bl i Respectively representing the bandwidth requirement and the buffer zone length requirement of the ith edge intelligent device in the edge intelligent device layer;
in order to meet the service quality requirement of the ith edge intelligent device, the user requirement and the resource allocation requirement are respectively set to be<Td i ,Cdr i >And<Bd i ,Bl i >；
the resource distributed by the ith edge intelligent device is in direct proportion to the resource required by the user, and the maximum resource R of the ith edge intelligent device is calculated i The formula (2) includes:
wherein,
representing the ratio of the bandwidth demand of the ith edge smart device to the maximum capacity of the edge smart gateway node,
the ratio of the requirement of the ith edge intelligent device on the length of the buffer zone to the total length of the buffer zone of the edge intelligent gateway is expressed;
and
the requirement ratio of the bandwidth and the buffer zone length of the ith edge intelligent device is respectively.
In the present invention, the constructing an end-to-end total delay function for all data packets on each of the edge intelligent devices according to the data transmission delay, the processing delay, and the queuing delay includes:
setting data transmission time delay t Td Data processing delay p Td And queuing delay q Td (ii) a Let the total number of packets on the ith intelligent node be
End-to-end total delay function Td i The method comprises the following steps:
wherein cld represents a cloud center, e-ctr and e-md represent an edge infrastructure layer and an edge smart device layer, respectively, a represents a constant duration time required for a single processor to complete a task, and epsilon and kappa represent an arrival rate and a service rate of a data packet, respectively.
In the present invention, the respectively constructing a data cumulative delivery rate function corresponding to each edge intelligent device according to the average queue length of the data packet on the edge intelligent device and the required buffer pool length thereof includes:
let the average queue length of the packets on the ith edge smart device be Aql i The required length of the buffer pool is Bl i /P size Then the cumulative delivery rate of the data packet Cdr i The method comprises the following steps:
in the present invention, the formula for constructing the optimal solution of resource allocation that simultaneously satisfies the maximization of the cumulative delivery rate of data, the minimization of total delay, and the maximization of the utilization rate of the total buffer length and the output link capacity of the edge intelligent gateway includes:
in another aspect of the present invention, there is also provided an internet of things data transmission management apparatus, including:
the system comprises a logic layering unit, a service layer and a service layer, wherein the logic layering unit is used for further dividing an edge layer in a cloud edge end network architecture in a framework model of the Internet of things into an edge infrastructure layer and an edge intelligent device layer; the edge intelligent devices of the edge intelligent device layer comprise various mobile intelligent devices in the edge layer, and the rest edge devices in the edge layer are edge infrastructure network facilities in the edge infrastructure layer;
the data type dividing unit is used for dividing the types of the data transmitted by the Internet of things into real-time data, near real-time data and non-real-time data according to different requirements on response speed and processing capacity; the real-time data comprises data needing to be processed by the edge intelligent equipment; the near real-time data comprises data requiring processing by an edge infrastructure in the edge infrastructure layer; the non-real-time data includes data that is neither the real-time data nor the near real-time data;
the path determining unit is used for determining a transmission path of the data to be transmitted in the Internet of things according to a preset rule; the preset rules include:
when the data to be transmitted is real-time data, forwarding the data to be transmitted to the nearest edge intelligent equipment in the edge intelligent equipment layer for processing; the edge intelligent equipment is used for generating result data, data to be transmitted of near real-time data or data to be transmitted of non-real-time data according to the data to be transmitted;
when the data to be transmitted is not real-time data, judging whether the data to be transmitted is near real-time data or not;
when the data to be transmitted is near real-time data, transmitting the near real-time data to an edge network infrastructure in the edge network infrastructure layer for processing, wherein the edge network infrastructure is used for generating result data or non-real-time data to be transmitted;
and when the data to be transmitted is not near real-time data, deleting the data to be transmitted as non-real-time data or transmitting the data to be transmitted to a cloud end layer for calculation processing.
In another aspect of the present invention, there is also provided a memory, which includes a software program, and the software program is adapted to be executed by a processor to perform the steps of the data transmission management method for the internet of things.
In another aspect of the embodiments of the present invention, there is also provided an internet of things data transmission management device, where the internet of things data transmission management device includes a computer program stored on a memory, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the method in the above aspects, and achieves the same technical effect.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, firstly, an edge layer in a cloud edge network architecture in a framework model of the Internet of things is further divided into an edge infrastructure layer and an edge intelligent equipment layer, and then the type of data transmitted by the Internet of things is divided into real-time data, near real-time data and non-real-time data; then, according to different types of data to be transmitted, corresponding transmission paths are set, and further, reasonable transmission paths are distributed according to different requirements of different types of data and applications on task processing and response time; therefore, unnecessary transmission of transmission data in the Internet of things can be effectively reduced (for example, the data volume transmitted to a cloud center can be reduced), so that the overall throughput of the Internet of things can be effectively reduced, the overall operation efficiency is improved, and the aim of improving the response speed of the Internet of things is finally fulfilled.
Furthermore, in the embodiment of the present invention, a system resource dynamic allocation policy is further designed, and a resource optimal dynamic allocation policy is constructed by using a relationship between a buffer length between an edge intelligent network node (such as an access site) and a plurality of mobile intelligent device nodes and an output link capacity, a total transmission delay of a data packet on the mobile intelligent device node, and a data accumulated successful delivery rate, so that the application system of the internet of things has a better service quality.
Further, in the embodiment of the present invention, a system load balancing scheme is also designed; in each network access layer (such as an edge infrastructure layer), a plurality of links formed by a plurality of different edge infrastructures are selected by using a link scheduler based on link distribution and link fusion technology, data streams are distributed to the links so as to reduce end-to-end transmission delay, and finally the data streams are aggregated at one end of the edge infrastructure layer close to a cloud center, so that the load balance of the system is realized.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood and to make the technical means implementable in accordance with the contents of the description, and to make the above and other objects, technical features, and advantages of the present invention more comprehensible, one or more preferred embodiments are described below in detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a step diagram of a data transmission management method of the internet of things in the invention;
fig. 2 is a schematic diagram of a hierarchical structure of a network architecture of the internet of things in the invention;
fig. 3 is a schematic flow chart of a data transmission management method of the internet of things in the invention;
FIG. 4 is a schematic diagram showing the comparison of the influence of the number of nodes of adjacent edge infrastructure on the task allocation of the system in the two IOT architectures;
FIG. 5 is a schematic diagram illustrating comparison of average latency for end-to-end data transmission and processing in the three IOT architectures of the present invention;
FIG. 6 is a diagram illustrating the effect of buffer size on packet delivery rate in different IOT architectures according to the present invention;
FIG. 7 is a schematic diagram illustrating the comparison of delay required when two architectures transmit the same amount of data in the three network environments of the present invention;
fig. 8 is a schematic structural diagram of a data transmission management device of the internet of things in the invention;
fig. 9 is a schematic structural diagram of the data transmission management device of the internet of things in the invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the term "comprise" or variations such as "comprises" or "comprising", etc., will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
As used herein, the terms "first," "second," and the like are used to distinguish two different elements or regions, and are not intended to define a particular position or relative relationship. In other words, the terms "first," "second," etc. may also be interchanged with one another in some embodiments.
Example one
In order to solve the problem that the response speed and quality of the internet of things are not ideal enough, as shown in fig. 1, the embodiment of the invention provides a data transmission management method for the internet of things, which is characterized by comprising the following steps:
s01, further dividing an edge layer in a cloud edge network architecture in a framework model of the Internet of things into an edge infrastructure layer and an edge intelligent device layer; the edge intelligent devices of the edge intelligent device layer comprise various mobile intelligent devices in the edge layer, and the rest edge devices in the edge layer are edge infrastructure network facilities in the edge infrastructure layer;
a framework model of the internet of things in the prior art is generally a cloud-edge network architecture, that is, the framework model comprises a terminal layer, an edge layer and a cloud-edge layer;
the inventor finds that, in the cloud edge network architecture environment in the prior art, the communication technology and the capability of various edge devices included in a primary insulating layer are different, and the roles in the data transmission process are also different; for example, compared with edge devices such as a switch, a router, and a routing switch, various mobile intelligent devices in a primary insulating layer often generate a large amount of data transmission, and are generally used for processing data that needs to be fed back to a user terminal in real time; therefore, the embodiment of the present invention improves the network architecture in the prior art, and further subdivides the primary insulating layer into an edge infrastructure layer and an edge smart device layer, so that various mobile smart devices in the edge layer are divided into the edge smart device layer as edge smart devices, and other edge devices are divided into the edge infrastructure layer as edge network infrastructures.
As shown in fig. 2, in the embodiment of the present invention, various mobile smart devices located in an edge smart device layer are collectively referred to as edge smart devices; the rest of the edge devices in the edge layer are classified as an edge infrastructure layer, which is collectively referred to as an edge infrastructure.
It should be noted that, in the internet of things in the embodiment of the present invention, an application scenario of the internet of things may be monitoring physical conditions of patients in smart house monitoring, local environment monitoring in a dangerous area, or medical monitoring (e.g., smart medical).
S02, dividing the types of the data transmitted by the Internet of things into real-time data, near real-time data and non-real-time data according to different requirements of response speed and processing capacity; wherein the real-time data comprises data which needs to be processed by the edge intelligent device; the near real-time data comprises data that needs to be processed by network devices in the edge infrastructure layer; the non-real-time data includes data that is neither the real-time data nor the near real-time data;
different edge devices are different in communication technology and capability, wherein the edge intelligent device is mainly used for processing data needing real-time processing; use thing networking to be applied to and doctorsed and nurses the control field (for example, wisdom medical treatment) as an example, when a certain mobile terminal who is marginal smart machine's effect does: reminding the user in real time after detecting the specific indication data of the patient user; at the moment, the specific indication data serving as data transmitted by the Internet of things can be defined as real-time data; the data processed by the edge infrastructure, which has a lower real-time requirement than the real-time data, is generally data that needs to be preprocessed in the edge infrastructure (e.g., a small data center or a data processing terminal in the edge infrastructure layer), and these data are called near real-time data; in practical applications, the near real-time data may be some intermediate result with a medical value generated according to a plurality of preset sign data.
S03, determining a transmission path of data to be transmitted in the Internet of things according to a preset rule;
after the edge infrastructure layer and the edge smart device layer are divided and the data types are divided, a preset rule for determining a transmission path of data to be transmitted in the internet of things is further set in the embodiment of the present invention, so as to achieve the purposes of reducing the overall throughput of the internet of things and improving the overall operation efficiency, specifically, referring to a flow diagram in fig. 3, in the embodiment of the present invention, the preset rule may specifically include the following multiple sub-steps:
s31, when the data to be transmitted is real-time data, the data to be transmitted is forwarded to the nearest edge intelligent device in the edge intelligent device layer for processing; the edge intelligent equipment is used for generating result data and data to be transmitted of near real-time data or data to be transmitted of non-real-time data according to the data to be transmitted;
for data (namely, data to be transmitted) which is generated by a terminal layer and needs to be transmitted through the internet of things, the data type of the data needs to be judged; when the data to be transmitted is real-time data, the data to be transmitted is forwarded to the nearest edge intelligent equipment in the edge intelligent equipment layer for corresponding processing; in the embodiment of the present invention, the processing result of the edge intelligent device on the real-time data may be result data generated for sending to a user terminal interface or a system interface, or to-be-transmitted data of near real-time data that needs to be further processed, or to-be-transmitted data that is non-real-time data.
When the data to be transmitted is not real-time data, further judging whether the data to be transmitted is near real-time data; when the data to be transmitted is near real-time data, the near real-time data is transmitted to an edge network infrastructure layer for processing, and an edge network infrastructure in the edge network infrastructure layer is used for generating result data or the data to be transmitted of non-real-time data;
in the embodiment of the present invention, the processing result of the edge network infrastructure on the near real-time data may be result data generated for sending to a user terminal interface or a system interface, or may be data to be transmitted as non-real-time data.
And when the data to be transmitted is not the near real-time data, deleting the data to be transmitted as the non-real-time data or transmitting the data to be transmitted to a cloud center of a cloud end layer for computing. Specifically, when the data to be transmitted is neither real-time data nor near-real-time data, the data to be transmitted is regarded as non-real-time data, and the non-real-time data is deleted or transmitted to a cloud center of a cloud end layer for calculation processing.
In summary, in the embodiment of the present invention, an edge layer in a cloud edge network architecture in a framework model of the internet of things is further divided into an edge infrastructure layer and an edge intelligent device layer, and then types of data transmitted by the internet of things are divided into real-time data, near-real-time data and non-real-time data; then, according to different types of data to be transmitted, corresponding transmission paths are set, and further, reasonable transmission paths are distributed according to different requirements of different types of data and applications on task processing and response time; therefore, unnecessary transmission of transmission data in the internet of things can be effectively reduced (for example, the data volume transmitted to the cloud center can be reduced), so that the overall throughput of the internet of things can be effectively reduced, the overall operation efficiency is improved, and the aim of improving the response speed of the internet of things is finally fulfilled.
Further, in the embodiment of the present invention, the method may further include a step of dynamically allocating a policy to the system resource, specifically:
s21, respectively designing the maximum resource allocation strategy of each edge intelligent device governed by the edge intelligent gateway according to the total buffer area length and the output link capacity of the edge intelligent gateway;
in practical applications, the step may specifically include:
setting the total buffer area length and the output link capacity of the edge intelligent gateway node to be L and C respectively, wherein the edge intelligent device layer is connected with M edge intelligent devices; p size Is the size of one packet on each edge smart device; bd i 、Bl i Respectively representing the bandwidth requirement and the buffer zone length requirement of the ith edge intelligent device in the edge intelligent device layer;
in order to meet the service quality requirement of the ith edge intelligent device, the user requirement and the resource allocation requirement are respectively set to be<Td i ,Cdr i >And<Bd i ,Bl i >；
the resource distributed by the ith edge intelligent device is in direct proportion to the resource required by the user, and the maximum resource R of the ith edge intelligent device is calculated i The formula (2) includes:
wherein,
representing the ratio of the bandwidth demand of the ith edge smart device to the maximum capacity of the edge smart gateway node,
the ratio of the requirement of the ith edge intelligent device on the length of the buffer zone to the total length of the buffer zone of the edge intelligent gateway is expressed;
and
the requirement ratio of the bandwidth and the buffer zone length of the ith edge intelligent device is respectively.
S22, respectively constructing an end-to-end total delay function for all data packets on each edge intelligent device according to data transmission delay, processing delay and queuing delay;
in practical application, the step may specifically include:
setting data transmission time delay t Td Data processing delay p Td And queuing delay q Td (ii) a Let the total number of packets on the ith intelligent node be
End-to-end total delay function Td i The method comprises the following steps:
wherein cld represents a cloud center, e-ctr and e-md respectively represent an edge infrastructure layer and an edge intelligent device layer, a represents a constant duration time required by a single processor to complete a task, and epsilon and kappa respectively refer to an arrival rate and a service rate of a data packet.
S23, respectively constructing a data accumulation delivery rate function corresponding to each edge intelligent device according to the average queue length of the data packets on the edge intelligent device and the required buffer pool length;
in practical applications, the step may specifically include:
let the average queue length of the data packets on the ith edge smart device be Aql i The required length of the buffer pool is Bl i /P size Then the accumulated delivery rate of the data packet Cdr i The method comprises the following steps:
s24, aiming at all edge intelligent devices in the scope of the edge intelligent gateway, constructing a resource allocation optimal scheme which simultaneously meets the maximization of data accumulated delivery rate, the minimization of total time delay and the maximization of the utilization rate of the total buffer area length and the output link capacity of the edge intelligent gateway.
In practical application, the step may specifically determine the optimal resource allocation scheme through the following formula:
as can be seen from the above, in the embodiment of the present invention, a system resource dynamic allocation policy is further designed, and a resource optimal dynamic allocation policy is constructed according to a relationship between the buffer length and the output link capacity between an edge intelligent gateway node (such as an access site) and a plurality of mobile intelligent device nodes, a total transmission delay of a data packet on the mobile intelligent device node, and a data accumulated successful delivery rate, so that the application system of the internet of things has a better service quality.
Further, in the embodiment of the present invention, the method may further include a step of implementing a system load balancing scheme, specifically:
s31, the link scheduling program determines a preset number of links according to user requirements and constructs each link self-adaptive optimization function;
s32, determining the priority corresponding to the number to be transmitted according to the transmission rate requirement and the queuing delay requirement of the number to be transmitted; the priorities are divided according to the data transmission rate requirement and the queuing delay requirement;
s33, respectively calculating the bandwidth or buffer length corresponding to each link and allocating to the link corresponding to the data to be transmitted according to the priority of the data to be transmitted.
It can be seen from the above that, in the embodiment of the present invention, a system load balancing scheme may also be designed; in each network access layer (such as an edge infrastructure layer), a plurality of links formed by a plurality of different nodes (such as edge infrastructures) are selected by using a link scheduler based on link distribution and link fusion technology, data streams are distributed to the links so as to reduce end-to-end transmission delay, and finally the data streams are aggregated at one end, close to a cloud center, of the edge infrastructure layer, so that load balance of the system is realized.
Furthermore, in the embodiment of the invention, the data transmission performance of the cloud edge system can be measured through a model performance evaluation function; specifically, task distribution statistics, data packet cumulative delivery rate and transmission delay are basic standards for measuring the quality of data transmission performance of the internet of things under the cooperation of the cloud side system, so that the data cumulative delivery rate function, the average transmission delay function and the task distribution condition constructed in the embodiment of the invention can be used as standards for evaluating the performance of the model, and the advantages of the embodiment of the invention are embodied by comparing with other models.
In practical application, MATLAB can be used for simulation experiments, the accumulated delivery rate of data packets, the average time delay and the task distribution are used as indexes for measuring the effect of the invention, and compared with the similar framework in the prior art, the embodiment of the invention has better data transmission performance by referring to fig. 4 to fig. 7.
In fig. 4, the impact of different numbers of neighboring edge infrastructure nodes on system task allocation on a network architecture with only edge infrastructure layers and both edge infrastructure layers and cloud centers is shown. Simulation results show that no matter which network architecture is adopted, when more adjacent edge infrastructure nodes are included in the data hierarchical transmission process, the load of each edge infrastructure node and the load of the cloud center are gradually reduced along with the improvement of the overall processing performance of the edge infrastructure nodes. For example, when there is only one neighboring edge infrastructure node, the system load of only the edge infrastructure layer is about 55%, the cloud center load of both is about 73%, and when the number of neighboring edge infrastructure nodes is 9, the corresponding load is exponentially decreased to 21% and 36%, respectively.
In fig. 5, the average latency of data transmission and processing from end to end in the architecture of the internet of things with different network hierarchies is shown. Simulation results show that when the edge mobile intelligent device node and the edge infrastructure node participate in task computing together with the cloud center, end-to-end data transmission delay is minimum. That is, the participation of more edge mobile smart devices and adjacent edge infrastructure resources may reduce end-to-end latency, as this process reduces data queuing time and transmission delays. Obviously, as the number of neighboring edge infrastructure nodes increases, the delay of task computation and data transmission gradually decreases.
In fig. 6, the impact of the system buffer size on the cumulative delivery rate of transmitted packets is shown for an internet of things architecture with different network hierarchies or different numbers of edge infrastructure nodes. Obviously, with the gradual expansion of the buffer area, no matter what kind of architecture of the internet of things system is provided, the cumulative delivery rate of the transmitted data packets increases, and accordingly, the data packet loss rate decreases. In fact, however, an increase in buffer size also typically increases the latency of task data queuing and the overall latency. Therefore, to ensure that the system has a higher delivery rate and lower transmission latency, a suitable buffer size must be selected.
In fig. 7, a comparison between the framework proposed by the present invention and the internet of things framework based on unpaired edge (fog) calculation subdivision in data transmission delay in three different network transmission environments, namely, light load, medium load and heavy load of the system, is shown. Experimental results show that, in any system environment, when the same data volume is transmitted, the data hierarchical transmission scheme proposed herein always takes the least time and has faster processing speed compared to the data transmission based on the framework proposed in the reference. For example, when a data volume of 80KB is transmitted, the average transmission delay of data in the three network environments of light load, medium load and heavy load is about 2.5 ms, 3.4 ms and 5.7 ms, respectively, and the transmission speed is improved by about 36%, 23.5% and 1.7% respectively compared with the frames in the references under the same condition. The above experiment fully illustrates the importance and necessity of subdividing the edge layer into an edge mobile intelligent device layer and an edge infrastructure layer according to the type of data to be processed, wherein the mobile intelligent device layer is used for processing real-time data of a small size, and the edge infrastructure layer can process near real-time data which is less sensitive to delay, and even part of non-real-time big data.
Example two
In another aspect of the embodiment of the present invention, a data transmission management device of the internet of things is further provided, and fig. 8 is a schematic structural diagram of the data transmission management device of the internet of things provided in the embodiment of the present invention, where the data transmission management device of the internet of things is a device corresponding to the data transmission management method of the internet of things in the embodiment corresponding to fig. 1, that is, the data transmission management method of the internet of things in the embodiment corresponding to fig. 1 is implemented by using a virtual device, and each virtual module constituting the data transmission management device of the internet of things can be executed by an electronic device, such as a network device, a terminal device, or a server. Specifically, the internet of things data transmission management device in the embodiment of the present invention includes:
the logic layering unit 01 is used for further dividing an edge layer in a cloud edge network architecture in a framework model of the Internet of things into an edge infrastructure layer and an edge intelligent device layer; the edge intelligent devices of the edge intelligent device layer comprise various mobile intelligent devices in the edge layer, and the rest edge devices in the edge layer are edge infrastructure network facilities in the edge infrastructure layer;
the data type dividing unit 02 is used for dividing the types of the data transmitted by the internet of things into real-time data, near real-time data and non-real-time data according to different requirements of response speed and processing capacity; wherein the real-time data comprises data which needs to be processed by the edge intelligent device; the near real-time data comprises data that requires edge infrastructure processing in the edge infrastructure layer; the non-real-time data includes data that does not belong to either the real-time data or the near real-time data;
the path determining unit 03 is configured to determine a transmission path of data to be transmitted in the internet of things according to a preset rule; the preset rules include:
when the data to be transmitted is real-time data, forwarding the data to be transmitted to the nearest edge intelligent equipment in the edge intelligent equipment layer for processing; the edge intelligent equipment is used for generating result data and data to be transmitted of near real-time data or data to be transmitted of non-real-time data according to the data to be transmitted;
when the data to be transmitted is not real-time data, judging whether the data to be transmitted is near real-time data or not;
when the data to be transmitted is near real-time data, the near real-time data is transmitted to an edge network infrastructure in the edge network infrastructure layer for processing, and the edge network infrastructure is used for generating result data or the data to be transmitted of non-real-time data;
and when the data to be transmitted is not near real-time data, deleting the data to be transmitted as non-real-time data or transmitting the data to be transmitted to a cloud end layer for calculation processing.
Since the working principle and the beneficial effects of the data transmission management device of the internet of things in the embodiment of the present invention have been recorded and described in the data transmission management method of the internet of things corresponding to fig. 1, they may be referred to each other and are not described herein again.
EXAMPLE III
In an embodiment of the present invention, a memory is further provided, where the memory includes a software program, and the software program is suitable for a processor to execute each step of the internet of things data transmission management method corresponding to fig. 1.
The embodiment of the present invention may be implemented by a software program, that is, by writing a software program (and an instruction set) for implementing each step in the data transmission management method for the internet of things corresponding to fig. 1, the software program is stored in a storage device, and the storage device is disposed in a computer device, so that a processor of the computer device may call the software program to implement the purpose of the embodiment of the present invention.
Example four
In an embodiment of the present invention, an internet of things data transmission management device is further provided, where a memory included in the internet of things data transmission management device includes a corresponding computer program product, and when a program instruction included in the computer program product is executed by a computer, the computer may execute the internet of things data transmission management method in the above aspects, and achieve the same technical effect.
Fig. 9 is a schematic diagram of a hardware structure of an internet of things data transmission management device as an electronic device according to an embodiment of the present invention, and as shown in fig. 9, the device includes one or more processors 610, a bus 630, and a memory 620. Taking one processor 610 as an example, the apparatus may further include: input device 640, output device 650.
The processor 610, the memory 620, the input device 640, and the output device 650 may be connected by a bus or other means, and fig. 9 illustrates the connection by a bus as an example.
The memory 620, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 610 executes various functional applications of the electronic device and data processing, i.e., a processing method implementing the above-described method embodiments, by executing non-transitory software programs, instructions, and modules stored in the memory 620.
The memory 620 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like. Further, the memory 620 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 620 optionally includes memory located remotely from the processor 610, which may be connected to the processing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 640 may receive input numeric or character information and generate a signal input. The output device 650 may include a display device such as a display screen.
The one or more modules are stored in the memory 620 and, when executed by the one or more processors 610, perform:
s01, further dividing an edge layer in a cloud edge network architecture in a framework model of the Internet of things into an edge infrastructure layer and an edge intelligent device layer; the edge intelligent devices of the edge intelligent device layer comprise various mobile intelligent devices in the edge layer, and the rest edge devices in the edge layer are edge infrastructure network facilities in the edge infrastructure layer;
s02, dividing the types of the data transmitted by the Internet of things into real-time data, near real-time data and non-real-time data according to different requirements of response speed and processing capacity; wherein the real-time data comprises data which needs to be processed by the edge intelligent device; the near real-time data comprises data requiring processing by an edge infrastructure in the edge infrastructure layer; the non-real-time data includes data that is neither the real-time data nor the near real-time data;
s03, determining a transmission path of data to be transmitted in the Internet of things according to a preset rule; the preset rules include:
when the data to be transmitted is real-time data, forwarding the data to be transmitted to the nearest edge intelligent equipment in the edge intelligent equipment layer for processing; the edge intelligent equipment is used for generating result data and data to be transmitted of near real-time data or data to be transmitted of non-real-time data according to the data to be transmitted;
when the data to be transmitted is not real-time data, judging whether the data to be transmitted is near real-time data or not;
when the data to be transmitted is near real-time data, the near real-time data is transmitted to an edge network infrastructure in the edge network infrastructure layer for processing, and the edge network infrastructure is used for generating result data or the data to be transmitted of non-real-time data;
and when the data to be transmitted is not the near real-time data, deleting the data to be transmitted as the non-real-time data or transmitting the data to be transmitted to a cloud end layer for calculation processing.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided in the embodiment of the present invention.
In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention, which is substantially or partly contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage device and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage device includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a ReRAM, an MRAM, a PCM, a NAND Flash, a NOR Flash, a Memory, a magnetic disk, an optical disk, or other various media that can store program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A data transmission management method of the Internet of things is characterized by comprising the following steps:
s01, further dividing an edge layer in a cloud edge network architecture in a framework model of the Internet of things into an edge infrastructure layer and an edge intelligent device layer; the edge intelligent devices of the edge intelligent device layer comprise various mobile intelligent devices in the edge layer, and the rest edge devices in the edge layer are edge infrastructure network facilities in the edge infrastructure layer;
s02, dividing the types of the data transmitted by the Internet of things into real-time data, near real-time data and non-real-time data according to different requirements of response speed and processing capacity; wherein the real-time data comprises data which needs to be processed by the edge intelligent device; the near real-time data comprises data requiring processing by an edge infrastructure in the edge infrastructure layer; the non-real-time data includes data that is neither the real-time data nor the near real-time data;
s03, determining a transmission path of the data to be transmitted in the Internet of things according to a preset rule; the preset rules include:
when the data to be transmitted is real-time data, forwarding the data to be transmitted to the nearest edge intelligent equipment in the edge intelligent equipment layer for processing; the edge intelligent equipment is used for generating result data, data to be transmitted of near real-time data or data to be transmitted of non-real-time data according to the data to be transmitted;
when the data to be transmitted is not real-time data, judging whether the data to be transmitted is near real-time data or not;
when the data to be transmitted is near real-time data, the near real-time data is transmitted to an edge infrastructure in the edge network infrastructure layer for processing; the edge network infrastructure is used for generating result data or data to be transmitted of non-real-time data;
and when the data to be transmitted is not near real-time data, deleting the data to be transmitted as non-real-time data or transmitting the data to be transmitted to a cloud end layer for calculation processing.
2. The internet of things data transmission management method of claim 1, further comprising:
respectively designing a maximum resource allocation strategy of each edge intelligent device governed by an edge intelligent gateway according to the total buffer area length and the output link capacity of the edge intelligent gateway;
respectively constructing an end-to-end total delay function for all data packets on each edge intelligent device according to data transmission delay, processing delay and queuing delay;
respectively constructing a data accumulated delivery rate function corresponding to each edge intelligent device according to the average queue length of the data packet on the edge intelligent device and the required buffer pool length;
and aiming at all edge intelligent devices in the scope governed by the edge intelligent gateway, constructing a resource allocation optimal scheme which simultaneously meets the conditions of maximizing the data accumulated delivery rate, minimizing the total time delay and maximizing the utilization rate of the total buffer area length and the output link capacity of the edge intelligent gateway.
3. The internet of things data transmission management method of claim 1 or 2, further comprising:
the link scheduling program determines a preset number of links according to user requirements and constructs a self-adaptive optimization function of each link;
determining the priority corresponding to the number to be transmitted according to the transmission rate requirement and the queuing time delay requirement of the number to be transmitted; the priorities are divided according to the data transmission rate requirement and the queuing delay requirement;
respectively calculating the bandwidth or the buffer area length corresponding to each link and distributing the bandwidth or the buffer area length to the link corresponding to the data to be transmitted according to the priority of the data to be transmitted.
4. The internet of things data transmission management method according to claim 2, wherein the step of designing the maximum resource allocation strategy of each edge intelligent device governed by the edge intelligent gateway according to the total buffer length and the output link capacity of the edge intelligent gateway comprises the steps of:
setting the total buffer area length and the output link capacity of the edge intelligent gateway node to be L and C respectively, wherein the edge intelligent device layer is connected with M edge intelligent devices; p size Is the size of one packet on each edge smart device; bd i 、Bl i Respectively representing the bandwidth requirement and the buffer zone length requirement of the ith edge intelligent device in the edge intelligent device layer;
in order to meet the service quality requirement of the ith edge intelligent device, the user requirement and the resource allocation requirement are respectively set to be<Td i ,Cdr i >And<Bd i ,Bl i >；
the resource distributed by the ith edge intelligent device is in direct proportion to the resource required by the user, and the maximum resource R of the ith edge intelligent device is calculated i The formula (2) includes:
wherein,
representing the ratio of the bandwidth requirement of the ith edge intelligent device to the maximum capacity of the edge intelligent gateway node,
the ratio of the requirement of the ith edge intelligent device on the length of the buffer zone to the total length of the buffer zone of the edge intelligent gateway is expressed;
and
the requirement ratio of the bandwidth and the buffer zone length of the ith edge intelligent device is respectively.
5. The internet of things data transmission management method according to claim 4, wherein the step of constructing an end-to-end total delay function for all data packets on each edge intelligent device according to data transmission delay, processing delay and queuing delay comprises the following steps:
setting data transmission time delay t Td Data processing delay p Td And queuing delay q Td (ii) a Let the total number of packets on the ith intelligent node be
End-to-end total delay function Td i The method comprises the following steps:
wherein cld represents a cloud center, e-ctr and e-md respectively represent an edge infrastructure layer and an edge intelligent device layer, a represents a constant duration time required by a single processor to complete a task, and epsilon and kappa respectively refer to an arrival rate and a service rate of a data packet.
6. The internet of things data transmission management method according to claim 5, wherein the step of respectively constructing the data cumulative delivery rate function corresponding to each edge intelligent device according to the average queue length of the data packets on the edge intelligent device and the required buffer pool length thereof comprises the steps of:
let the average queue length of the packets on the ith edge smart device be Aql i The required buffer pool length is Bl i /P size Then the cumulative delivery rate of the data packet Cdr i The method comprises the following steps:
7. the internet of things data transmission management method of claim 6, wherein the formula for constructing the optimal solution of resource allocation which simultaneously satisfies the maximization of data cumulative delivery rate, the minimization of total delay, and the maximization of utilization rate of total buffer length and output link capacity of the edge intelligent gateway comprises:
8. the utility model provides a thing networking data transmission management device which characterized in that includes:
the system comprises a logic layering unit, a service layer and a service layer, wherein the logic layering unit is used for further dividing an edge layer in a cloud edge end network architecture in a framework model of the Internet of things into an edge infrastructure layer and an edge intelligent device layer; the edge intelligent devices of the edge intelligent device layer comprise various mobile intelligent devices in the edge layer, and the rest edge devices in the edge layer are edge infrastructure network facilities in the edge infrastructure layer;
the data type dividing unit is used for dividing the types of the data transmitted by the Internet of things into real-time data, near real-time data and non-real-time data according to different requirements of response speed and processing capacity; wherein the real-time data comprises data which needs to be processed by the edge intelligent device; the near real-time data comprises data requiring processing by an edge infrastructure in the edge infrastructure layer; the non-real-time data includes data that does not belong to either the real-time data or the near real-time data;
the path determining unit is used for determining a transmission path of the data to be transmitted in the Internet of things according to a preset rule; the preset rules comprise:
when the data to be transmitted is real-time data, forwarding the data to be transmitted to the nearest edge intelligent equipment in the edge intelligent equipment layer for processing; the edge intelligent equipment is used for generating result data and data to be transmitted of near real-time data or data to be transmitted of non-real-time data according to the data to be transmitted;
when the data to be transmitted is not real-time data, judging whether the data to be transmitted is near real-time data or not;
when the data to be transmitted is near real-time data, the near real-time data is transmitted to an edge network infrastructure in the edge network infrastructure layer for processing, and the edge network infrastructure is used for generating result data or the data to be transmitted of non-real-time data;
and when the data to be transmitted is not near real-time data, deleting the data to be transmitted as non-real-time data or transmitting the data to be transmitted to a cloud end layer for calculation processing.
9. A memory comprising a software program adapted to be executed by a processor for performing the steps of the internet of things data transmission management method according to any one of claims 1 to 7.
10. An internet of things data transmission management device comprising a bus, a processor and a memory as claimed in claim 9;
the bus is used for connecting the memory and the processor;
the processor is configured to execute a set of instructions in the memory.
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